Fundamentals of Analytics Engineering

Analytics engineering sits at the point where reliable data systems meet the questions a business needs to answer. Fundamentals of Analytics Engineering gives that emerging discipline a practical, end-to-end frame: seven practitioners explain the work, the tools, and the team practices that help turn raw data into something people can use with confidence.

Understand the role—and the modern data stack

The opening chapters define analytics engineering, distinguish it from data analysis and data engineering, and trace the shift from ETL toward ELT. They also examine the Modern Data Stack: why cloud-native, modular tools can make analytics more accessible, and what trade-offs come with that approach.

Follow data from source to insight

The book then follows the work through data ingestion, cloud data warehousing, data modeling, transformation, and serving data through dashboards and business intelligence tools. This progression helps readers see how each stage connects to the next, rather than treating pipelines, models, and reporting as separate concerns.

A hands-on platform-building section

A dedicated guide walks through building a data platform, including work with Google Cloud, dbt Cloud, Tableau, and key performance indicators. The practical example gives the preceding concepts a concrete setting and shows how platform components can come together in an analytics workflow.

Make the work dependable and collaborative

Later chapters turn to data quality and observability, team coding practices, version control, code review, CI/CD, workflow automation, and documentation. The book also addresses scoping analytics use cases, encouraging business adoption, and applying data governance—essential considerations when data products need to be trusted and maintained over time.

Who will find it useful?

Data analysts and data engineers exploring analytics engineering will find a guided introduction to the role and its responsibilities. Practicing analytics engineers can use the broad coverage to review their approach and identify topics to develop further. The authors recommend a basic grounding in data analysis and engineering concepts, including data cleaning, visualization, ETL, and data warehousing.

With its combination of foundational explanations, a platform-building example, and team-focused practices, this book offers a clear route through the connected work of modern analytics engineering.